A Computational Bipartite Graph-Based Drug Repurposing Method

Zheng, S. Q.; Ma, Hetong; Wang, Jiayang; Li, Jiao

doi:10.1007/978-1-4939-8955-3_7

Cited by 9 publications

(5 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, a possible mechanism proposed is that the activated clofazamine leads to the release of reactive oxygen species (ROS) causing bacterial death [40]. Another proposed mechanism is that clofazimine acts as a membrane-destabilizing agent leading to the dysfunction of membrane transporters [41].…”

Section: Pathogen-targeted Therapymentioning

confidence: 99%

An overview of current strategies and future prospects in drug repurposing in tuberculosis

Singh

Chawla

2023

Exploration of Medicine

View full text Add to dashboard Cite

A large number of the population faces mortality as an effect of tuberculosis (TB). The line of treatment in the management of TB faces a jolt with ever-increasing multi-drug resistance (DR) cases. Further, the drugs engaged in the treatment of TB are associated with different toxicities, such as renal and hepatic toxicity. Different combinations are sought for effective anti-tuberculosis (anti-TB) effects with a decrease in toxicity. In this regard, drug repurposing has been very promising in improving the efficacy of drugs by enhancement of bioavailability and widening the safety margin. The success in drug repurposing lies in specified binding and inhibition of a particular target in the drug molecule. Different drugs have been repurposed for various ailments like cancer, Alzheimer’s disease, acquired immunodeficiency syndrome (AIDS), hair loss, etc. Repurposing in anti-TB drugs holds great potential too. The use of whole-cell screening assays and the availability of large chemical compounds for testing against Mycobacterium tuberculosis poses a challenge in this development. The target-based discovery of sites has emerged in the form of phenotypic screening as ethionamide R (EthR) and malate synthase inhibitors are similar to pharmaceuticals. In this review, the authors have thoroughly described the drug repurposing techniques on the basis of pharmacogenomics and drug metabolism, pathogen-targeted therapy, host-directed therapy, and bioinformatics approaches for the identification of drugs. Further, the significance of repurposing of drugs elaborated on large databases has been revealed. The role of genomics and network-based methods in drug repurposing has been also discussed in this article.

show abstract

Section: Pathogen-targeted Therapymentioning

confidence: 99%

An overview of current strategies and future prospects in drug repurposing in tuberculosis

Singh

Chawla

2023

Exploration of Medicine

View full text Add to dashboard Cite

show abstract

“…Since bipartite graphs are frequently utilised in many different application domains [many of them in biology and medicine (Pavlopoulos et al, 2018 ; Ma et al, 2023 ) and drug discovery an repurposing (Zheng et al, 2018 ; Manoochehri and Nourani, 2020 ; Hostallero et al, 2022 ; Yu et al, 2022 )], bipartite graph embedding has recently received a lot of interest. The majority of earlier techniques, which use random walk- or reconstruction-based objectives, are usually successful at learning local graph topologies.…”

Section: Graph Embedding Algorithmsmentioning

confidence: 99%

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Lecca,

Lecca

2023

Front. Artif. Intell.

View full text Add to dashboard Cite

Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g., social, citations, and trademark networks) aim to implement various data mining tasks including classification, clustering, recommendation, anomaly detection, and link prediction. The commitment and efforts of artificial intelligence research in network biology are motivated by the fact that machine learning techniques are often prohibitively computational demanding, low parallelizable, and ultimately inapplicable, since biological network of realistic size is a large system, which is characterised by a high density of interactions and often with a non-linear dynamics and a non-Euclidean latent geometry. Currently, graph embedding emerges as the new learning paradigm that shifts the tasks of building complex models for classification, clustering, and link prediction to learning an informative representation of the graph data in a vector space so that many graph mining and learning tasks can be more easily performed by employing efficient non-iterative traditional models (e.g., a linear support vector machine for the classification task). The great potential of graph embedding is the main reason of the flourishing of studies in this area and, in particular, the artificial intelligence learning techniques. In this mini review, we give a comprehensive summary of the main graph embedding algorithms in light of the recent burgeoning interest in geometric deep learning.

show abstract

“…Repurposing needs a lot of data from various perspectives [80, 81]. To feed this, [83][84][85][86][87]. Though these models have promised better performance, still there predictions are not conceived yet [82].…”

Section: Use Of Ai In Drug Repurposingmentioning

confidence: 99%

A comprehensive review on the application of artificial intelligence in drug discovery.

Sahoo

Dar

2021

TABCJ

View full text Add to dashboard Cite

The 21st century is witnessing immense achievements in human history, starting from home science to space science. Artificial Intelligence (AI) is a salient one among these feats, the critical factor of the 4th industrial revolution. Health is the primary and essential asset for the continuity of human civilization on this planet. Not only must we address the deadly existing diseases like Cancer, AIDS, Alzheimer's, heart diseases, gastrointestinal diseases, etc., but on top of that, we must effectively predict, prevent and respond to potential pathogens capable of causing havoc like the recent outbreak caused by SARS-CoV-2. AI-enabled technology with the computational capacity of a computer and reasoning ability of humans saves surplus labor and time that is majorly consumed in target validation, lead optimization, molecular representation, and designing reaction pathways, which traditionally is a decade-long way of searching, visualizing, studying, imagining, experimenting and maintaining a ton of data. This article would focus on how AI will help find the drug-like properties in the compound screening phase predicting the Structure-Activity Relationship (SAR) and ADMET properties in lead identification and optimization phases, sustainable development of chemicals in the synthesis phases up to AI's assistance in the successful conduct of clinical trials and repurposing.

show abstract

A Computational Bipartite Graph-Based Drug Repurposing Method

Cited by 9 publications

References 47 publications

An overview of current strategies and future prospects in drug repurposing in tuberculosis

An overview of current strategies and future prospects in drug repurposing in tuberculosis

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

A comprehensive review on the application of artificial intelligence in drug discovery.

Contact Info

Product

Resources

About